Overview

Dataset statistics

Number of variables23
Number of observations129880
Missing cells393
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 MiB
Average record size in memory180.0 B

Variable types

Categorical6
Numeric17

Alerts

seat_comfort is highly overall correlated with food_and_drinkHigh correlation
departure_arrival_time_convenient is highly overall correlated with food_and_drink and 1 other fieldsHigh correlation
food_and_drink is highly overall correlated with seat_comfort and 2 other fieldsHigh correlation
gate_location is highly overall correlated with departure_arrival_time_convenient and 1 other fieldsHigh correlation
inflight_wifi_service is highly overall correlated with online_support and 2 other fieldsHigh correlation
inflight_entertainment is highly overall correlated with satisfactionHigh correlation
online_support is highly overall correlated with inflight_wifi_service and 2 other fieldsHigh correlation
ease_of_online_booking is highly overall correlated with inflight_wifi_service and 2 other fieldsHigh correlation
on_board_service is highly overall correlated with cleanlinessHigh correlation
cleanliness is highly overall correlated with on_board_serviceHigh correlation
online_boarding is highly overall correlated with inflight_wifi_service and 2 other fieldsHigh correlation
departure_delay_in_minutes is highly overall correlated with arrival_delay_in_minutesHigh correlation
arrival_delay_in_minutes is highly overall correlated with departure_delay_in_minutesHigh correlation
satisfaction is highly overall correlated with inflight_entertainmentHigh correlation
type_of_travel is highly overall correlated with classHigh correlation
class is highly overall correlated with type_of_travelHigh correlation
seat_comfort has 4797 (3.7%) zerosZeros
departure_arrival_time_convenient has 6664 (5.1%) zerosZeros
food_and_drink has 5945 (4.6%) zerosZeros
inflight_entertainment has 2978 (2.3%) zerosZeros
departure_delay_in_minutes has 73356 (56.5%) zerosZeros
arrival_delay_in_minutes has 72753 (56.0%) zerosZeros

Reproduction

Analysis started2023-07-14 13:41:50.809115
Analysis finished2023-07-14 13:44:38.703430
Duration2 minutes and 47.89 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
1
71087 
0
58793 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129880
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 71087
54.7%
0 58793
45.3%

Length

2023-07-14T14:44:38.979625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T14:44:39.416163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 71087
54.7%
0 58793
45.3%

Most occurring characters

ValueCountFrequency (%)
1 71087
54.7%
0 58793
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 71087
54.7%
0 58793
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 129880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 71087
54.7%
0 58793
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 71087
54.7%
0 58793
45.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Female
65899 
Male
63981 

Length

Max length6
Median length6
Mean length5.0147675
Min length4

Characters and Unicode

Total characters651318
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 65899
50.7%
Male 63981
49.3%

Length

2023-07-14T14:44:39.821280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T14:44:40.325453image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
female 65899
50.7%
male 63981
49.3%

Most occurring characters

ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 521438
80.1%
Uppercase Letter 129880
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 195779
37.5%
a 129880
24.9%
l 129880
24.9%
m 65899
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 65899
50.7%
M 63981
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 651318
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 651318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 195779
30.1%
a 129880
19.9%
l 129880
19.9%
F 65899
 
10.1%
m 65899
 
10.1%
M 63981
 
9.8%

customer_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Loyal Customer
106100 
disloyal Customer
23780 

Length

Max length17
Median length14
Mean length14.549276
Min length14

Characters and Unicode

Total characters1889660
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowLoyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 106100
81.7%
disloyal Customer 23780
 
18.3%

Length

2023-07-14T14:44:40.621297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T14:44:40.983764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
customer 129880
50.0%
loyal 106100
40.8%
disloyal 23780
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o 259760
13.7%
l 153660
 
8.1%
s 153660
 
8.1%
y 129880
 
6.9%
a 129880
 
6.9%
129880
 
6.9%
C 129880
 
6.9%
u 129880
 
6.9%
t 129880
 
6.9%
m 129880
 
6.9%
Other values (5) 413420
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1523800
80.6%
Uppercase Letter 235980
 
12.5%
Space Separator 129880
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 259760
17.0%
l 153660
10.1%
s 153660
10.1%
y 129880
8.5%
a 129880
8.5%
u 129880
8.5%
t 129880
8.5%
m 129880
8.5%
e 129880
8.5%
r 129880
8.5%
Other values (2) 47560
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 129880
55.0%
L 106100
45.0%
Space Separator
ValueCountFrequency (%)
129880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1759780
93.1%
Common 129880
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 259760
14.8%
l 153660
8.7%
s 153660
8.7%
y 129880
7.4%
a 129880
7.4%
C 129880
7.4%
u 129880
7.4%
t 129880
7.4%
m 129880
7.4%
e 129880
7.4%
Other values (4) 283540
16.1%
Common
ValueCountFrequency (%)
129880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1889660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 259760
13.7%
l 153660
 
8.1%
s 153660
 
8.1%
y 129880
 
6.9%
a 129880
 
6.9%
129880
 
6.9%
C 129880
 
6.9%
u 129880
 
6.9%
t 129880
 
6.9%
m 129880
 
6.9%
Other values (5) 413420
21.9%

age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.427957
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:41.295819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.11936
Coefficient of variation (CV)0.38346801
Kurtosis-0.71914023
Mean39.427957
Median Absolute Deviation (MAD)12
Skewness-0.0036062117
Sum5120903
Variance228.59505
MonotonicityNot monotonic
2023-07-14T14:44:41.658762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 3692
 
2.8%
25 3511
 
2.7%
40 3209
 
2.5%
44 3104
 
2.4%
41 3089
 
2.4%
42 3017
 
2.3%
43 2941
 
2.3%
45 2939
 
2.3%
23 2935
 
2.3%
22 2931
 
2.3%
Other values (65) 98512
75.8%
ValueCountFrequency (%)
7 685
0.5%
8 797
0.6%
9 859
0.7%
10 822
0.6%
11 837
0.6%
12 794
0.6%
13 806
0.6%
14 860
0.7%
15 1006
0.8%
16 1156
0.9%
ValueCountFrequency (%)
85 25
 
< 0.1%
80 110
0.1%
79 52
 
< 0.1%
78 44
 
< 0.1%
77 106
0.1%
76 60
 
< 0.1%
75 76
 
0.1%
74 61
 
< 0.1%
73 67
 
0.1%
72 249
0.2%

type_of_travel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Business travel
89693 
Personal Travel
40187 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1948200
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowPersonal Travel
3rd rowPersonal Travel
4th rowPersonal Travel
5th rowPersonal Travel

Common Values

ValueCountFrequency (%)
Business travel 89693
69.1%
Personal Travel 40187
30.9%

Length

2023-07-14T14:44:42.022716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T14:44:42.385020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
travel 129880
50.0%
business 89693
34.5%
personal 40187
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s 309266
15.9%
e 259760
13.3%
r 170067
8.7%
a 170067
8.7%
l 170067
8.7%
n 129880
6.7%
129880
6.7%
v 129880
6.7%
B 89693
 
4.6%
u 89693
 
4.6%
Other values (5) 299947
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1648253
84.6%
Uppercase Letter 170067
 
8.7%
Space Separator 129880
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 309266
18.8%
e 259760
15.8%
r 170067
10.3%
a 170067
10.3%
l 170067
10.3%
n 129880
7.9%
v 129880
7.9%
u 89693
 
5.4%
i 89693
 
5.4%
t 89693
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 89693
52.7%
P 40187
23.6%
T 40187
23.6%
Space Separator
ValueCountFrequency (%)
129880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1818320
93.3%
Common 129880
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 309266
17.0%
e 259760
14.3%
r 170067
9.4%
a 170067
9.4%
l 170067
9.4%
n 129880
7.1%
v 129880
7.1%
B 89693
 
4.9%
u 89693
 
4.9%
i 89693
 
4.9%
Other values (4) 210254
11.6%
Common
ValueCountFrequency (%)
129880
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1948200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 309266
15.9%
e 259760
13.3%
r 170067
8.7%
a 170067
8.7%
l 170067
8.7%
n 129880
6.7%
129880
6.7%
v 129880
6.7%
B 89693
 
4.6%
u 89693
 
4.6%
Other values (5) 299947
15.4%

class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
Business
62160 
Eco
58309 
Eco Plus
9411 

Length

Max length8
Median length8
Mean length5.7552741
Min length3

Characters and Unicode

Total characters747495
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco
2nd rowBusiness
3rd rowEco
4th rowEco
5th rowEco

Common Values

ValueCountFrequency (%)
Business 62160
47.9%
Eco 58309
44.9%
Eco Plus 9411
 
7.2%

Length

2023-07-14T14:44:42.807354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T14:44:43.163272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
eco 67720
48.6%
business 62160
44.6%
plus 9411
 
6.8%

Most occurring characters

ValueCountFrequency (%)
s 195891
26.2%
u 71571
 
9.6%
E 67720
 
9.1%
c 67720
 
9.1%
o 67720
 
9.1%
B 62160
 
8.3%
i 62160
 
8.3%
n 62160
 
8.3%
e 62160
 
8.3%
9411
 
1.3%
Other values (2) 18822
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 598793
80.1%
Uppercase Letter 139291
 
18.6%
Space Separator 9411
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 195891
32.7%
u 71571
 
12.0%
c 67720
 
11.3%
o 67720
 
11.3%
i 62160
 
10.4%
n 62160
 
10.4%
e 62160
 
10.4%
l 9411
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 67720
48.6%
B 62160
44.6%
P 9411
 
6.8%
Space Separator
ValueCountFrequency (%)
9411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 738084
98.7%
Common 9411
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 195891
26.5%
u 71571
 
9.7%
E 67720
 
9.2%
c 67720
 
9.2%
o 67720
 
9.2%
B 62160
 
8.4%
i 62160
 
8.4%
n 62160
 
8.4%
e 62160
 
8.4%
P 9411
 
1.3%
Common
ValueCountFrequency (%)
9411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 195891
26.2%
u 71571
 
9.6%
E 67720
 
9.1%
c 67720
 
9.1%
o 67720
 
9.1%
B 62160
 
8.3%
i 62160
 
8.3%
n 62160
 
8.3%
e 62160
 
8.3%
9411
 
1.3%
Other values (2) 18822
 
2.5%

flight_distance
Real number (ℝ)

Distinct5398
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1981.4091
Minimum50
Maximum6951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:43.515673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile341
Q11359
median1925
Q32544
95-th percentile3831
Maximum6951
Range6901
Interquartile range (IQR)1185

Descriptive statistics

Standard deviation1027.1156
Coefficient of variation (CV)0.51837636
Kurtosis0.36430599
Mean1981.4091
Median Absolute Deviation (MAD)594
Skewness0.46674752
Sum2.5734541 × 108
Variance1054966.5
MonotonicityNot monotonic
2023-07-14T14:44:43.887737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1963 92
 
0.1%
1812 88
 
0.1%
1639 87
 
0.1%
1981 86
 
0.1%
1789 86
 
0.1%
1766 83
 
0.1%
1759 83
 
0.1%
1748 82
 
0.1%
2022 81
 
0.1%
1769 81
 
0.1%
Other values (5388) 129031
99.3%
ValueCountFrequency (%)
50 23
< 0.1%
51 21
< 0.1%
52 21
< 0.1%
53 28
< 0.1%
54 21
< 0.1%
55 22
< 0.1%
56 30
< 0.1%
57 21
< 0.1%
58 15
< 0.1%
59 24
< 0.1%
ValueCountFrequency (%)
6951 1
< 0.1%
6950 1
< 0.1%
6948 1
< 0.1%
6924 1
< 0.1%
6907 2
< 0.1%
6889 1
< 0.1%
6882 1
< 0.1%
6868 1
< 0.1%
6865 1
< 0.1%
6837 1
< 0.1%

seat_comfort
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8385972
Minimum0
Maximum5
Zeros4797
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:44.533712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3929832
Coefficient of variation (CV)0.49072946
Kurtosis-0.94319309
Mean2.8385972
Median Absolute Deviation (MAD)1
Skewness-0.091860998
Sum368677
Variance1.9404023
MonotonicityNot monotonic
2023-07-14T14:44:44.785646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 29183
22.5%
2 28726
22.1%
4 28398
21.9%
1 20949
16.1%
5 17827
13.7%
0 4797
 
3.7%
ValueCountFrequency (%)
0 4797
 
3.7%
1 20949
16.1%
2 28726
22.1%
3 29183
22.5%
4 28398
21.9%
5 17827
13.7%
ValueCountFrequency (%)
5 17827
13.7%
4 28398
21.9%
3 29183
22.5%
2 28726
22.1%
1 20949
16.1%
0 4797
 
3.7%

departure_arrival_time_convenient
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9906452
Minimum0
Maximum5
Zeros6664
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:45.047435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5272244
Coefficient of variation (CV)0.51066718
Kurtosis-1.089371
Mean2.9906452
Median Absolute Deviation (MAD)1
Skewness-0.25228245
Sum388425
Variance2.3324143
MonotonicityNot monotonic
2023-07-14T14:44:45.354375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29593
22.8%
5 26817
20.6%
3 23184
17.9%
2 22794
17.6%
1 20828
16.0%
0 6664
 
5.1%
ValueCountFrequency (%)
0 6664
 
5.1%
1 20828
16.0%
2 22794
17.6%
3 23184
17.9%
4 29593
22.8%
5 26817
20.6%
ValueCountFrequency (%)
5 26817
20.6%
4 29593
22.8%
3 23184
17.9%
2 22794
17.6%
1 20828
16.0%
0 6664
 
5.1%

food_and_drink
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8519941
Minimum0
Maximum5
Zeros5945
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:45.643631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4437294
Coefficient of variation (CV)0.50621751
Kurtosis-0.98672754
Mean2.8519941
Median Absolute Deviation (MAD)1
Skewness-0.11681295
Sum370417
Variance2.0843545
MonotonicityNot monotonic
2023-07-14T14:44:45.906114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28150
21.7%
4 27216
21.0%
2 27146
20.9%
1 21076
16.2%
5 20347
15.7%
0 5945
 
4.6%
ValueCountFrequency (%)
0 5945
 
4.6%
1 21076
16.2%
2 27146
20.9%
3 28150
21.7%
4 27216
21.0%
5 20347
15.7%
ValueCountFrequency (%)
5 20347
15.7%
4 27216
21.0%
3 28150
21.7%
2 27146
20.9%
1 21076
16.2%
0 5945
 
4.6%

gate_location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9904219
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:46.199501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3059699
Coefficient of variation (CV)0.4367176
Kurtosis-1.0898225
Mean2.9904219
Median Absolute Deviation (MAD)1
Skewness-0.053063895
Sum388396
Variance1.7055574
MonotonicityNot monotonic
2023-07-14T14:44:46.542014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 33546
25.8%
4 30088
23.2%
2 24518
18.9%
1 22565
17.4%
5 19161
14.8%
0 2
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 22565
17.4%
2 24518
18.9%
3 33546
25.8%
4 30088
23.2%
5 19161
14.8%
ValueCountFrequency (%)
5 19161
14.8%
4 30088
23.2%
3 33546
25.8%
2 24518
18.9%
1 22565
17.4%
0 2
 
< 0.1%

inflight_wifi_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.24913
Minimum0
Maximum5
Zeros132
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:46.927685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3188175
Coefficient of variation (CV)0.40589867
Kurtosis-1.1214461
Mean3.24913
Median Absolute Deviation (MAD)1
Skewness-0.19112285
Sum421997
Variance1.7392797
MonotonicityNot monotonic
2023-07-14T14:44:47.312776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 31560
24.3%
5 28830
22.2%
3 27602
21.3%
2 27045
20.8%
1 14711
11.3%
0 132
 
0.1%
ValueCountFrequency (%)
0 132
 
0.1%
1 14711
11.3%
2 27045
20.8%
3 27602
21.3%
4 31560
24.3%
5 28830
22.2%
ValueCountFrequency (%)
5 28830
22.2%
4 31560
24.3%
3 27602
21.3%
2 27045
20.8%
1 14711
11.3%
0 132
 
0.1%

inflight_entertainment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3834771
Minimum0
Maximum5
Zeros2978
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:47.698185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3460591
Coefficient of variation (CV)0.39783309
Kurtosis-0.53278592
Mean3.3834771
Median Absolute Deviation (MAD)1
Skewness-0.60482822
Sum439446
Variance1.8118752
MonotonicityNot monotonic
2023-07-14T14:44:48.071466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 41879
32.2%
5 29831
23.0%
3 24200
18.6%
2 19183
14.8%
1 11809
 
9.1%
0 2978
 
2.3%
ValueCountFrequency (%)
0 2978
 
2.3%
1 11809
 
9.1%
2 19183
14.8%
3 24200
18.6%
4 41879
32.2%
5 29831
23.0%
ValueCountFrequency (%)
5 29831
23.0%
4 41879
32.2%
3 24200
18.6%
2 19183
14.8%
1 11809
 
9.1%
0 2978
 
2.3%

online_support
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5197028
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:48.477901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3065107
Coefficient of variation (CV)0.37119915
Kurtosis-0.81057183
Mean3.5197028
Median Absolute Deviation (MAD)1
Skewness-0.57536498
Sum457139
Variance1.7069702
MonotonicityNot monotonic
2023-07-14T14:44:48.901391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 41510
32.0%
5 35563
27.4%
3 21609
16.6%
2 17260
13.3%
1 13937
 
10.7%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 13937
 
10.7%
2 17260
13.3%
3 21609
16.6%
4 41510
32.0%
5 35563
27.4%
ValueCountFrequency (%)
5 35563
27.4%
4 41510
32.0%
3 21609
16.6%
2 17260
13.3%
1 13937
 
10.7%
0 1
 
< 0.1%

ease_of_online_booking
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.472105
Minimum0
Maximum5
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:49.268775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.3055596
Coefficient of variation (CV)0.37601387
Kurtosis-0.91065426
Mean3.472105
Median Absolute Deviation (MAD)1
Skewness-0.49171965
Sum450957
Variance1.704486
MonotonicityNot monotonic
2023-07-14T14:44:49.663419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 39920
30.7%
5 34137
26.3%
3 22418
17.3%
2 19951
15.4%
1 13436
 
10.3%
0 18
 
< 0.1%
ValueCountFrequency (%)
0 18
 
< 0.1%
1 13436
 
10.3%
2 19951
15.4%
3 22418
17.3%
4 39920
30.7%
5 34137
26.3%
ValueCountFrequency (%)
5 34137
26.3%
4 39920
30.7%
3 22418
17.3%
2 19951
15.4%
1 13436
 
10.3%
0 18
 
< 0.1%

on_board_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4650755
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:50.027176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2708356
Coefficient of variation (CV)0.36675553
Kurtosis-0.78502308
Mean3.4650755
Median Absolute Deviation (MAD)1
Skewness-0.50526988
Sum450044
Variance1.6150231
MonotonicityNot monotonic
2023-07-14T14:44:50.403148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 40675
31.3%
5 31724
24.4%
3 27037
20.8%
2 17174
13.2%
1 13265
 
10.2%
0 5
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 13265
 
10.2%
2 17174
13.2%
3 27037
20.8%
4 40675
31.3%
5 31724
24.4%
ValueCountFrequency (%)
5 31724
24.4%
4 40675
31.3%
3 27037
20.8%
2 17174
13.2%
1 13265
 
10.2%
0 5
 
< 0.1%

leg_room_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4859024
Minimum0
Maximum5
Zeros444
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:50.765815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.292226
Coefficient of variation (CV)0.37070057
Kurtosis-0.84132096
Mean3.4859024
Median Absolute Deviation (MAD)1
Skewness-0.49644007
Sum452749
Variance1.669848
MonotonicityNot monotonic
2023-07-14T14:44:51.153131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 39698
30.6%
5 34385
26.5%
3 22467
17.3%
2 21745
16.7%
1 11141
 
8.6%
0 444
 
0.3%
ValueCountFrequency (%)
0 444
 
0.3%
1 11141
 
8.6%
2 21745
16.7%
3 22467
17.3%
4 39698
30.6%
5 34385
26.5%
ValueCountFrequency (%)
5 34385
26.5%
4 39698
30.6%
3 22467
17.3%
2 21745
16.7%
1 11141
 
8.6%
0 444
 
0.3%

baggage_handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1014.8 KiB
4
48240 
5
35748 
3
24485 
2
13432 
1
7975 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters129880
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Length

2023-07-14T14:44:51.579339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-14T14:44:52.065450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring characters

ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129880
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 129880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 48240
37.1%
5 35748
27.5%
3 24485
18.9%
2 13432
 
10.3%
1 7975
 
6.1%

checkin_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3408069
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:52.462250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2605823
Coefficient of variation (CV)0.37732869
Kurtosis-0.79351105
Mean3.3408069
Median Absolute Deviation (MAD)1
Skewness-0.39244248
Sum433904
Variance1.5890677
MonotonicityNot monotonic
2023-07-14T14:44:52.867301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 36481
28.1%
3 35538
27.4%
5 27005
20.8%
2 15486
11.9%
1 15369
11.8%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 15369
11.8%
2 15486
11.9%
3 35538
27.4%
4 36481
28.1%
5 27005
20.8%
ValueCountFrequency (%)
5 27005
20.8%
4 36481
28.1%
3 35538
27.4%
2 15486
11.9%
1 15369
11.8%
0 1
 
< 0.1%

cleanliness
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7057592
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:53.241844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1517739
Coefficient of variation (CV)0.31080647
Kurtosis-0.20888866
Mean3.7057592
Median Absolute Deviation (MAD)1
Skewness-0.75600069
Sum481304
Variance1.3265831
MonotonicityNot monotonic
2023-07-14T14:44:53.637635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 48795
37.6%
5 35916
27.7%
3 23984
18.5%
2 13412
 
10.3%
1 7768
 
6.0%
0 5
 
< 0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 7768
 
6.0%
2 13412
 
10.3%
3 23984
18.5%
4 48795
37.6%
5 35916
27.7%
ValueCountFrequency (%)
5 35916
27.7%
4 48795
37.6%
3 23984
18.5%
2 13412
 
10.3%
1 7768
 
6.0%
0 5
 
< 0.1%

online_boarding
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.352587
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:54.010904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2987145
Coefficient of variation (CV)0.38737682
Kurtosis-0.93804992
Mean3.352587
Median Absolute Deviation (MAD)1
Skewness-0.36649561
Sum435434
Variance1.6866594
MonotonicityNot monotonic
2023-07-14T14:44:54.398427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 35181
27.1%
3 30780
23.7%
5 29973
23.1%
2 18573
14.3%
1 15359
11.8%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 15359
11.8%
2 18573
14.3%
3 30780
23.7%
4 35181
27.1%
5 29973
23.1%
ValueCountFrequency (%)
5 29973
23.1%
4 35181
27.1%
3 30780
23.7%
2 18573
14.3%
1 15359
11.8%
0 14
 
< 0.1%

departure_delay_in_minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct466
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.713713
Minimum0
Maximum1592
Zeros73356
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:54.896344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile77
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.071126
Coefficient of variation (CV)2.5874589
Kurtosis100.64455
Mean14.713713
Median Absolute Deviation (MAD)0
Skewness6.8219803
Sum1911017
Variance1449.4107
MonotonicityNot monotonic
2023-07-14T14:44:55.450689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73356
56.5%
1 3682
 
2.8%
2 2855
 
2.2%
3 2535
 
2.0%
4 2309
 
1.8%
5 2136
 
1.6%
6 1884
 
1.5%
7 1748
 
1.3%
8 1618
 
1.2%
9 1552
 
1.2%
Other values (456) 36205
27.9%
ValueCountFrequency (%)
0 73356
56.5%
1 3682
 
2.8%
2 2855
 
2.2%
3 2535
 
2.0%
4 2309
 
1.8%
5 2136
 
1.6%
6 1884
 
1.5%
7 1748
 
1.3%
8 1618
 
1.2%
9 1552
 
1.2%
ValueCountFrequency (%)
1592 1
< 0.1%
1305 1
< 0.1%
1128 1
< 0.1%
1017 1
< 0.1%
978 1
< 0.1%
951 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
921 1
< 0.1%
859 1
< 0.1%

arrival_delay_in_minutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct472
Distinct (%)0.4%
Missing393
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.091129
Minimum0
Maximum1584
Zeros72753
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1014.8 KiB
2023-07-14T14:44:56.040180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile78
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.46565
Coefficient of variation (CV)2.5488915
Kurtosis95.117114
Mean15.091129
Median Absolute Deviation (MAD)0
Skewness6.6701246
Sum1954105
Variance1479.6062
MonotonicityNot monotonic
2023-07-14T14:44:56.525063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72753
56.0%
1 2747
 
2.1%
2 2587
 
2.0%
3 2442
 
1.9%
4 2373
 
1.8%
5 2083
 
1.6%
6 2021
 
1.6%
7 1794
 
1.4%
8 1751
 
1.3%
9 1566
 
1.2%
Other values (462) 37370
28.8%
ValueCountFrequency (%)
0 72753
56.0%
1 2747
 
2.1%
2 2587
 
2.0%
3 2442
 
1.9%
4 2373
 
1.8%
5 2083
 
1.6%
6 2021
 
1.6%
7 1794
 
1.4%
8 1751
 
1.3%
9 1566
 
1.2%
ValueCountFrequency (%)
1584 1
< 0.1%
1280 1
< 0.1%
1115 1
< 0.1%
1011 1
< 0.1%
970 1
< 0.1%
952 1
< 0.1%
940 1
< 0.1%
924 1
< 0.1%
920 1
< 0.1%
860 1
< 0.1%

Interactions

2023-07-14T14:44:27.125491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:09.087983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:17.204614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:25.788704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:33.977350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:42.035380image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:52.895179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:05.822575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:12.948078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:21.085029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:33.294048image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:42.209082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:49.635468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:56.729568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:04.848709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:11.796427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:19.214998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:27.550435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:09.507790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:17.696706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:26.235275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:34.350947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:42.651291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:53.651962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:06.420287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:13.442934image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:21.547388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:34.042413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:42.610994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:50.107012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:57.256687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:05.342868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:12.179988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:19.730947image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:27.970436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:09.974011image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:18.264948image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:26.688973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:34.816680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:43.366330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:54.364484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:06.958133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:13.941239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:22.057130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:34.703689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:42.969838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:50.590996image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:57.877265image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:05.833188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:12.563488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:20.255512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:28.330526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:10.489125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:18.625029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:27.102371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:43:14.402512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:22.453578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:35.190368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:43.296136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:51.013415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:58.317342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:06.277455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:12.908902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:20.752431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:28.715227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:10.945076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:19.029695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:27.562229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:35.685014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:44.868699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:43:07.778294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:43:23.143999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:35.810109image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:43.671006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:51.486407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:58.777730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:06.719780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:44:21.248083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:42:45.448937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:56.625241image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:43:08.535021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:15.770216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:43:36.801570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:43:52.362505image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:44:07.609184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:44:22.269447image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:29.974892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-14T14:42:14.285194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:23.198913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:31.283364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:38.796977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:48.785361image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:01.988683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:10.351563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:18.063678image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:28.470836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:39.407353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:46.961704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:54.077693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:02.044829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:09.499725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:16.051599image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:24.814335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:32.252989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:14.723686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:23.557145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:31.635086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:39.325261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:49.246225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:02.563716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:10.715981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:18.503546image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:29.274779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:39.956760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:47.388426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:54.487544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:02.464063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:09.831905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:16.951275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:25.176652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:32.713072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:15.103422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:23.928355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:32.134423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:39.785368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:49.773980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:03.083198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:11.090214image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:18.962965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:29.960608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:40.375090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:47.805523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:55.063932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:02.894861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:10.177289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:17.409013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:25.535782image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:33.185220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:15.475804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:24.260188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:32.641535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:40.225192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:50.520248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:03.603099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:11.462073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:19.457742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:30.656400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:40.849477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:48.221975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:55.410155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:03.329816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:10.600088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:17.843250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:25.892400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:33.680704image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:15.970296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:24.616739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:33.069009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:40.735070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:51.377376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:04.144858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:11.931138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:19.941149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:31.502288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:41.274918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:48.668341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:55.838661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:03.783278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:11.019041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:18.245026image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:26.284528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:34.215589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:16.633700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:25.131481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:33.483218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:41.393431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:42:52.187797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:04.769596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:12.408788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:20.540273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:32.521507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:41.732306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:49.156651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:43:56.292982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:04.320912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:11.412686image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:18.713924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-14T14:44:26.721766image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-14T14:44:57.090479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ageflight_distanceseat_comfortdeparture_arrival_time_convenientfood_and_drinkgate_locationinflight_wifi_serviceinflight_entertainmentonline_supportease_of_online_bookingon_board_serviceleg_room_servicecheckin_servicecleanlinessonline_boardingdeparture_delay_in_minutesarrival_delay_in_minutessatisfactiongendercustomer_typetype_of_travelclassbaggage_handling
age1.000-0.2480.0090.0380.015-0.0010.0130.1340.1230.0760.0780.0930.036-0.0040.039-0.009-0.0110.2040.0150.3780.3420.2080.045
flight_distance-0.2481.000-0.0470.001-0.011-0.0040.005-0.037-0.035-0.026-0.029-0.0290.0040.0100.0040.0560.0390.2000.1770.2570.1590.2150.032
seat_comfort0.009-0.0471.0000.4390.7050.4100.1290.3990.1200.2020.1180.1240.0440.1060.131-0.031-0.0390.4700.1210.0740.0580.0570.131
departure_arrival_time_convenient0.0380.0010.4391.0000.5380.555-0.0040.063-0.003-0.0040.0590.0220.0660.077-0.003-0.005-0.0070.0400.0630.2880.2110.0670.061
food_and_drink0.015-0.0110.7050.5381.0000.5340.0230.3220.0250.0350.0370.0610.0120.0330.012-0.013-0.0160.2650.0900.0890.0830.0610.042
gate_location-0.001-0.0040.4100.5550.5341.000-0.004-0.0040.0020.000-0.024-0.007-0.032-0.010-0.0030.0050.0060.1430.0380.1460.0780.0820.051
inflight_wifi_service0.0130.0050.129-0.0040.023-0.0041.0000.2600.5340.5800.0590.0310.0880.0490.617-0.022-0.0320.2450.0360.0970.0300.0610.043
inflight_entertainment0.134-0.0370.3990.0630.322-0.0040.2601.0000.4610.3400.2060.1750.2370.1470.370-0.035-0.0510.6400.1540.2480.0920.1820.104
online_support0.123-0.0350.120-0.0030.0250.0020.5340.4611.0000.6040.1740.1510.2110.1270.650-0.022-0.0380.4330.0940.2000.0660.1390.093
ease_of_online_booking0.076-0.0260.202-0.0040.0350.0000.5800.3400.6041.0000.4650.3770.1370.4520.663-0.032-0.0470.4540.0860.1620.0440.1010.375
on_board_service0.078-0.0290.1180.0590.037-0.0240.0590.2060.1740.4651.0000.4260.2380.5790.139-0.030-0.0490.3610.0650.1040.0520.1240.415
leg_room_service0.093-0.0290.1240.0220.061-0.0070.0310.1750.1510.3770.4261.0000.1560.4230.110-0.010-0.0250.3360.0980.1250.0700.1040.312
checkin_service0.0360.0040.0440.0660.012-0.0320.0880.2370.2110.1370.2380.1561.0000.2500.177-0.019-0.0350.2810.0180.0460.0610.1130.144
cleanliness-0.0040.0100.1060.0770.033-0.0100.0490.1470.1270.4520.5790.4230.2501.0000.118-0.038-0.0590.3050.0320.0530.0680.1030.500
online_boarding0.0390.0040.131-0.0030.012-0.0030.6170.3700.6500.6630.1390.1100.1770.1181.000-0.020-0.0340.3500.0590.1290.0330.0900.073
departure_delay_in_minutes-0.0090.056-0.031-0.005-0.0130.005-0.022-0.035-0.022-0.032-0.030-0.010-0.019-0.038-0.0201.0000.7400.0440.0020.0000.0040.0000.005
arrival_delay_in_minutes-0.0110.039-0.039-0.007-0.0160.006-0.032-0.051-0.038-0.047-0.049-0.025-0.035-0.059-0.0340.7401.0000.0450.0000.0000.0000.0000.006
satisfaction0.2040.2000.4700.0400.2650.1430.2450.6400.4330.4540.3610.3360.2810.3050.3500.0440.0451.0000.2120.2930.1090.3120.310
gender0.0150.1770.1210.0630.0900.0380.0360.1540.0940.0860.0650.0980.0180.0320.0590.0020.0000.2121.0000.0310.0090.0120.038
customer_type0.3780.2570.0740.2880.0890.1460.0970.2480.2000.1620.1040.1250.0460.0530.1290.0000.0000.2930.0311.0000.3080.1230.064
type_of_travel0.3420.1590.0580.2110.0830.0780.0300.0920.0660.0440.0520.0700.0610.0680.0330.0040.0000.1090.0090.3081.0000.5540.061
class0.2080.2150.0570.0670.0610.0820.0610.1820.1390.1010.1240.1040.1130.1030.0900.0000.0000.3120.0120.1230.5541.0000.107
baggage_handling0.0450.0320.1310.0610.0420.0510.0430.1040.0930.3750.4150.3120.1440.5000.0730.0050.0060.3100.0380.0640.0610.1071.000

Missing values

2023-07-14T14:44:35.189925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-14T14:44:37.256487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

satisfactiongendercustomer_typeagetype_of_travelclassflight_distanceseat_comfortdeparture_arrival_time_convenientfood_and_drinkgate_locationinflight_wifi_serviceinflight_entertainmentonline_supportease_of_online_bookingon_board_serviceleg_room_servicebaggage_handlingcheckin_servicecleanlinessonline_boardingdeparture_delay_in_minutesarrival_delay_in_minutes
01FemaleLoyal Customer65Personal TravelEco2650002242330353200.0
11MaleLoyal Customer47Personal TravelBusiness246400030223444232310305.0
21FemaleLoyal Customer15Personal TravelEco21380003202233444200.0
31FemaleLoyal Customer60Personal TravelEco6230003343110141300.0
41FemaleLoyal Customer70Personal TravelEco3540003434220242500.0
51MaleLoyal Customer30Personal TravelEco18940003202254554200.0
61FemaleLoyal Customer66Personal TravelEco227000325555055531715.0
71MaleLoyal Customer10Personal TravelEco18120003202233454200.0
81FemaleLoyal Customer56Personal TravelBusiness730003535440154400.0
91MaleLoyal Customer22Personal TravelEco1556000320222453423026.0
satisfactiongendercustomer_typeagetype_of_travelclassflight_distanceseat_comfortdeparture_arrival_time_convenientfood_and_drinkgate_locationinflight_wifi_serviceinflight_entertainmentonline_supportease_of_online_bookingon_board_serviceleg_room_servicebaggage_handlingcheckin_servicecleanlinessonline_boardingdeparture_delay_in_minutesarrival_delay_in_minutes
1298701Femaledisloyal Customer70Personal TravelEco1674545155553245455446.0
1298711Femaledisloyal Customer35Personal TravelEco32875453252245443290.0
1298721Femaledisloyal Customer69Personal TravelEco22405453454454434440.0
1298731Femaledisloyal Customer63Personal TravelEco1942554434335253537NaN
1298741Femaledisloyal Customer11Personal TravelEco27525552252235354250.0
1298751Femaledisloyal Customer29Personal TravelEco17315553252233444200.0
1298760Maledisloyal Customer63Personal TravelBusiness208723242113233121174172.0
1298770Maledisloyal Customer69Personal TravelEco232030333224434232155163.0
1298780Maledisloyal Customer66Personal TravelEco245032323223323212193205.0
1298790Femaledisloyal Customer38Personal TravelEco430734333334555333185186.0